Oscar Pérez-Priego

Terrestrial gross primary production inferred from satellite fluorescence and vegetation models

Determining the spatial and temporal distribution of terrestrial gross primary production (GPP) is a critical step in closing the Earths carbon budget. Dynamical global vegetation models (DGVMs) provide mechanistic insight into GPP variability but diverge in predicting the response to climate in poorly investigated regions. Recent advances in the remote sensing of solar-induced chlorophyll fluorescence (SIF) opens up a new possibility to provide direct global observational constraints for GPP. Here, we apply an optimal estimation approach to infer the global distribution of GPP from an ensemble of eight DGVMs constrained by global measurements of SIF from the Greenhouse Gases Observing SATellite (GOSAT). These estimates are compared to flux tower data in N. America, Europe, and tropical S. America, with careful consideration of scale differences between models, GOSAT, and flux towers. Assimilation of GOSAT SIF with DGVMs causes a redistribution of global productivity from northern latitudes to the tropics of 7-8 Pg C yr(-1) from 2010 to 2012, with reduced GPP in northern forests (~3.6 Pg C yr(-1) ) and enhanced GPP in tropical forests (~3.7 Pg C yr(-1) ). This leads to improvements in the structure of the seasonal cycle, including earlier dry season GPP loss and enhanced peak-to-trough GPP in tropical forests within the Amazon Basin and reduced growing season length in northern croplands and deciduous forests. Uncertainty in predicted GPP (estimated from the spread of DGVMs) is reduced by 40-70% during peak productivity suggesting the assimilation of GOSAT SIF with models is well-suited for benchmarking. We conclude that satellite fluorescence augurs a new opportunity to quantify the GPP response to climate drivers and the potential to constrain predictions of carbon cycle evolution.

Influence of physiological phenology on the seasonal pattern of ecosystem respiration in deciduous forests

Understanding the environmental and biotic drivers of respiration at the ecosystem level is a prerequisite to further improve scenarios of the global carbon cycle. In this study we investigated the relevance of physiological phenology, defined as seasonal changes in plant physiological properties, for explaining the temporal dynamics of ecosystem respiration (RECO) in deciduous forests. Previous studies showed that empirical RECO models can be substantially improved by considering the biotic dependency of RECO on the short-term productivity (e.g., daily gross primary production, GPP) in addition to the well-known environmental controls of temperature and water availability. Here, we use a model-data integration approach to investigate the added value of physiological phenology, represented by the first temporal derivative of GPP, or alternatively of the fraction of absorbed photosynthetically active radiation, for modeling RECO at 19 deciduous broadleaved forests in the FLUXNET La Thuile database. The new data-oriented semiempirical model leads to an 8% decrease in root mean square error (RMSE) and a 6% increase in the modeling efficiency (EF) of modeled RECO when compared to a version of the model that does not consider the physiological phenology. The reduction of the model-observation bias occurred mainly at the monthly time scale, and in spring and summer, while a smaller reduction was observed at the annual time scale. The proposed approach did not improve the model performance at several sites, and we identified as potential causes the plant canopy heterogeneity and the use of air temperature as a driver of ecosystem respiration instead of soil temperature. However, in the majority of sites the model-error remained unchanged regardless of the driving temperature. Overall, our results point toward the potential for improving current approaches for modeling RECO in deciduous forests by including the phenological cycle of the canopy.

Winds induce CO2 exchange with the atmosphere and vadose zone transport in a karstic ecosystem

Andalusian regional government project GEOCARBO [P08-RNM-3721]; European Union ERDF funds; Spanish Ministry of Science and Innovation project SOILPROF [CGL2011-15276-E]; Spanish Ministry of Science and Innovation project CARBORAD [CGL2011-27493]; Spanish Ministry of Science and Innovation project GEISpain [CGL2014-52838-C2-1-R]; Marie Curie International Outgoing Fellowship within the 7th European Community Framework Programme, DIESEL project [625988]

Short- and mid-term tillage-induced soil CO2 efflux on irrigated permanent- and conventional-bed planting systems with controlled traffic in southern Spain

Use of permanent beds combined with controlled traffic (PB) has been proposed as an alternative planting systemfor reducingsoil erosionand compactionwhileincreasingsoil organic carbon (SOC)inirrigated, annual-cropbased systems in Mediterranean conditions. The objective of this study was to characterise, in space (beds and furrows with and without traffic) and time (hours, days, and weeks), soil CO2 efflux in PB compared with conventionally tilled bed planting (CB) and with a variant of PB in which subsoiling was performed in trafficked furrows (DPB). The three treatments were combined with controlled traffic. Tillage resulted in abrupt CO2 effluxes that lowered rapidly within hours. However, in CB, soil CO2 effluxes increased again significantly 12 days after tillage compared with PB or DPB. These differences were due to higher emissions from beds than from furrows where the soil had been compacted during the harrowing that formed the beds. In DPB, CO2 effluxes increased in furrows with traffic after subsoiling and the effect was maintained during the study despite subsequent traffic. Soil CO2efflux increased with soil temperature (measured concomitantly) except after soil tillage. Tillage reduced SOC in both CB and DPB compared with PB.

Using near-infrared-enabled digital repeat photography to track structural and physiological phenology in mediterranean tree–grass ecosystems

Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP.